Application Deadline for this workshop is October 20, 2017
This workshop will be held at the Penn Pavilion (Level 2), Duke University, Durham, NC.
This second workshop in the SAMSI Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applied Mathematics focusses on Monte Carlo sampling methods –an important class of computational algorithms for estimating high dimensional distributions. Monte Carlo sampling is widely used in physics, chemistry, mathematics and statistics, and is most useful when other methods fail due to the high dimensionality of the problem. Due to the extensive application of Monte Carlo sampling across disciplines, breakthroughs in one discipline can lead to advances in others.
This SAMSI workshop brings together experts from applied mathematics, statistics and machine learning for the purpose of exchanging ideas and advancing the broad area of sampling algorithms.
Schedule and Supporting Media
A schedule will be posted as the event approaches in 2017.
Confirmed Speakers currently include:
- Yves Atchade (University of Michigan)
- Tamara Broderick (MIT)
- Larry Carin (Duke University)
- Arnaud Doucet (Oxford University – GBR)
- Andrew Duncan (Sussex University – GBR)
- Andrew Gelman (Columbia University)
- James Johndrow (Stanford University)
- Omar Ghattas (University of Texas – Austin)
- Shiwei Lan (CalTech)
- Qiang Liu (Dartmouth)
- Lizhen Lin (Notre Dame University)
- Lester Mackey (Stanford University)
- Youssef Marzouk (MIT)
- Antonietta Mira (Università della Svizzera Italiana – CHE)
- Paris Perdikaris (MIT)
- Christian Robert (Université Paris-Dauphine – FRA)
- Andrew Stuart (CalTech)
- Eric Vanden-Eijnden (New York University)
- Jonathan Weare (University of Chicago)
- Clayton Webster (Oak Ridge National Lab)
More information will be made available as the event approaches in 2017.
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